AI + Data engineering (2026 Data Engineering Roadmap Phase 2) скачать в хорошем качестве

AI + Data engineering (2026 Data Engineering Roadmap Phase 2)

The Problem: Text-to-SQL AI systems promise to turn your LLM into a senior data analyst. But in reality, they fail 80-85% of the time on real-world data. This video exposes why benchmarks lie and how to build systems that actually work in production. What You'll Learn: • How Zillow's AI made a $562M mistake (and how to avoid it) • Why Spider 1.0 benchmarks don't predict real-world performance • The fan trap: How AI can report 500% wrong revenue numbers • The hidden token tax costing you thousands per month • The semantic firewall architecture that stops AI hallucinations • How to reduce AI inference costs by 80% with semantic caching Timestamps: 0:00 - Zillow's $562M AI Disaster 0:29 - The Probabilistic vs Deterministic Problem 1:28 - Why Text-to-SQL Benchmarks Are Misleading 2:10 - Spider 1.0 vs Real-World Data Warehouses 3:08 - LLMs Are Probabilistic, Databases Are Deterministic 4:00 - The Fan Trap Explained: 500% Wrong Revenue 5:20 - The Token Tax: Hidden Costs of Text-to-SQL 6:25 - The Anti-Fragile Solution: Semantic Firewall 8:15 - MCP: The New Standard for AI Data Access 8:50 - Semantic Caching: 80% Cost Reduction Critical Insights: • 90% benchmark accuracy ≠ 90% production accuracy • Real-world success rates: 15-20% without proper architecture • The token tax: 20K tokens just to explain your schema • 10-second latency kills user experience • Silent failures are worse than loud failures Who This Is For: • Data engineers building AI-powered analytics • Senior engineers architecting production AI systems • Teams implementing LLM-to-database solutions • Anyone tired of AI demos that fail in production Key Takeaway: Stop treating LLMs like DBAs. Treat them as reasoning engines. Use semantic layers as firewalls between AI and your data warehouse. Let the LLM write API calls, not SQL. Next Steps: Watch the full playlist to master anti-fragile data engineering and build systems that survive production. Don't build fragile demos—build career-defining infrastructure.